Another branch of approach employs deep learning technique [1]} to capture anomalous data in dynamic graphs, which is denoted as the category of deep learning-based method.
NetWalk [2]} leverages a random walk-based encoder to generate node embeddings with clique embedding objective and then models the network evolving via dynamic updating reservoirs. Finally, a dynamic clustering-based anomaly detector is employed to score the abnormality of each edge.
AddGraph [3]} further constructs an end-to-end neural network model to capture dynamic graphs' spatial and temporal patterns. A GCN [4]} is served as a structural features extractor, and a GRU-attention module is designed to combine short-term and long-term dynamic evolving.
StrGNN [5]} extracts the \(h\) -hop enclosing subgraph of edges and leverages stacked GCN [4]} and GRU to capture the spatial and temporal information. The learning model is trained in an end-to-end way with negative sampling from “context-dependent” noise distribution.
H-VGRAE [7]} builds a hierarchical model by combining variational graph autoencoder and recurrent neural network. To detect anomalous edges, the edge reconstruction probability is used to measure the abnormality.
